Nowadays, brain-inspired computing is among the most challenging information and communication technologies.
The electronic neuromorphic networks are implemented to reproduce brain-like processing applications wherein principles of computation based on pattern learning and recognition are performed by neural models. The neural models use synapses or synaptic circuits to connect neurons to each other for exchanging signals. A single neuron is connected with thousands of other neurons between synapses.
Therefore, the scaling down of the sizes and complexity of the artificial synaptic circuit is one of the important tasks in the design of the electronic neuromorphic network.
A known solution for pattern learning and recognition is via software.
As alternative with respect to the known solution, the neural models comprising neuroplasticity synaptic circuits are adapted for learning and for recognizing patterns allowing to develop small sizes and low-power circuits for portable applications, as cellular phone, smart-watches and automotive device, drones and similar devices.
Moreover, the electronic neuromorphic systems by comprising neuroplasticity synaptic circuits perform energy-autonomous devices that allow interactions with the real world. These systems can be used for real-time pattern recognition in order to develop applications in monitoring environments such as public places, security places and the like.
The neuroplasticity synaptic circuits comprise a nanoscale resistive switch or memristor having an electronically-tunable conductance.
By considering the synaptic circuits, the influence that a firing spike of a pre-synaptic neuron has on a post-synaptic neuron is indicated as the synaptic circuit weight. The weight of each synaptic circuit is plastic and timing variable and the mechanism of long-term weight adaptation is known as spike-timing dependent plasticity STDP and reflects the capacity of the synaptic circuit to communicate and to modify its state. In particular, a potentiated or a depressed state of the memristor is transferred as a Long-Time Potential LTP or a Long-Time Depression LTD by the synaptic circuit.
In order to achieve a multitask operation, a known solution proposes a time-division multiplexing TDM approach wherein neuron spikes follow a precise synchronous sequence for communication, long-term potentiation LTP and long-term depression LTD.
This known approach, although advantageous for many aspects, has some drawbacks. In actual fact, synchronous clocking may be practically difficult in case of large neuromorphic systems.
Another recent solution proposes a fully asynchronous approach for communication/learning of neuromorphic synapses by using leaky-integrate-and-fire neurons in order to obtain a biological brain, where synapses are potentiated/depressed through asynchronous spike timing dependent plasticity STDP. Also, this known approach is advantageous for many aspects, but has some drawbacks.
A known solution is disclosed in application No. WO2010133399A1 relating to an electronic learning synapse with spike-timing dependent plasticity using phase change memory. Another solution is disclosed in US application No. US20140358834 relating to a synapse circuit and neuromorphic system including the same. PCT application No. WO2012169726A1 discloses a synapse for function cell of spike timing dependent plasticity, function cell of STDP. US 2012/0084241A1 discloses producing spike-timing dependent plasticity in a neuromorphic network utilizing phase change synaptic device.
A satisfactory solution of memristor synapses circuits for communication and learning with reduced sizes and complexity and low power consumption has not been achieved.